Autonomous knowledge-oriented clustering using decision-theoretic rough set theory

Authors:
Hong Yu;Shuangshuang Chu;Dachun Yang
Affiliations:
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China;Chongqing R&D Institute, ZTE Corp., Chongqing, P.R. China
Venue:
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Year:
2010

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Abstract

This paper processes an autonomous knowledge-oriented clustering method based on the decision-theoretic rough set theory model. In order to get the initial clustering of knowledge-oriented clusterings, the threshold values are produced autonomously in view of physics theory in this paper rather than are subjected by human intervention. Furthermore, this paper proposes a cluster validity index based on the decision-theoretic rough set theory model by considering various loss functions. Experiments with synthetic and standard data show that the novel method is not only helpful to select a termination point of the clustering algorithm, but also is useful to cluster the overlapped boundaries which is common in many data mining applications.